Methods for deploying biosentinels to agricultural fields and monitoring biotic and abiotic stresses in crops remotely

ABSTRACT

One variation of a method for interpreting pressures in plants includes: accessing a first image of a first set of sentinel plants in a field; accessing a second image of a second set of sentinel plants in the field, recorded during a first period; interpreting a first pressure of a stressor in the first set based on features extracted from the first image, captured during the first period; interpreting a second pressure in the second set based on features extracted from the second image; deriving a model associating pressure at the first set and pressure at the second set based on the first pressure and the second pressure; interpreting a third pressure in the first set based on features extracted from a third image captured during a second period; and predicting a fourth pressure in the second set during the second period based on the third pressure and the model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a continuation of U.S. patent application Ser. No.17/592,275, filed on 3 Feb. 2022, which is a continuation of U.S. patentapplication Ser. No. 17/217,840, filed on 30 Mar. 2021, which is acontinuation of U.S. patent application Ser. No. 16/908,526, filed on 22Jun. 2020, which claims the benefit of U.S. Provisional Application No.62/864,401, filed on 20 Jun. 2019, each of which is incorporated in itsentirety by this reference.

TECHNICAL FIELD

This invention relates generally to the field of agriculture and morespecifically to a new and useful methods for deploying biosensors toagricultural fields and monitoring plant stressors in crops based onbiosensors in the field of agriculture.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of a method;

FIG. 2 is a flowchart representation of the method;

FIG. 3 is a flowchart representation of the method;

FIG. 4 is a graphical representation of the method;

FIG. 5 is a graphical representation of the method;

FIG. 6 is a graphical representation of the method; and

FIG. 7 is a schematic representation of a fixed optical sensor.

DESCRIPTION OF THE EMBODIMENTS

The following description of embodiments of the invention is notintended to limit the invention to these embodiments but rather toenable a person skilled in the art to make and use this invention.Variations, configurations, implementations, example implementations,and examples described herein are optional and are not exclusive to thevariations, configurations, implementations, example implementations,and examples they describe. The invention described herein can includeany and all permutations of these variations, configurations,implementations, example implementations, and examples.

1. Method

As shown in FIGS. 1-3 , a method S100 includes: accessing a first feedof images recorded at a first frequency by a fixed sensor facing a firstset of sentinel plants in an agricultural field in Block Silo; accessinga second image of a second set of sentinel plants in the agriculturalfield, the second image recorded by a mobile sensor during a first timeperiod in Block S112; interpreting a first pressure of a stressor in thefirst set of sentinel plants during the first time period based on afirst set of features extracted from a first image, in the first feed ofimages, captured during the first time period in Block S120;interpreting a second pressure of the stressor in the second set ofsentinel plants during the first time period based on a second set offeatures extracted from the second image in Block S122; deriving apressure model associating pressure of the stressor at the first set ofsentinel plants and pressure of the stressor at the second set ofsentinel plants based on the first pressure and the second pressure inBlock S130; interpreting a third pressure of the stressor in the firstset of sentinel plants during a second time period based on a third setof features extracted from a third image, in the first feed of images,captured during the second time period in Block S140; predicting afourth pressure of the stressor in the second set of sentinel plantsduring the second time period based on the third pressure and thepressure model in Block S150; and, in response to the fourth pressure inthe second set of sentinel plants exceeding a threshold pressure,generating a prompt to address the stressor in plants proximal thesecond set of sentinel plants in the agricultural field in Block S160.

As shown in FIGS. 2 and 3 , one variation of the method S100 includes:accessing a first feed of images recorded at a first frequency by afixed sensor facing a first set of sentinel plants in an agriculturalfield in Block Silo; accessing a second image of a region of theagricultural field comprising the first set of sentinel plants, thesecond image recorded by a mobile sensor during a first time period inBlock S114; interpreting a first pressure of a stressor in the first setof sentinel plants during the first time period based on a first set offeatures extracted from a first image, in the first feed of images,captured during the first time period in Block S120; interpreting afirst pressure gradient of the stressor in sentinel plants in the regionof the agricultural field during the first time period based on a secondset of features extracted from the second image in Block S124; derivinga gradient model associating pressure of the stressor at the first setof sentinel plants and pressure gradient of the stressor in the regionof the agricultural field based on the first pressure of the stressorand the first pressure gradient in Block S132; interpreting a secondpressure of the stressor in the first set of sentinel plants during asecond time period based on a third set of features extracted from athird image, in the first feed of images, captured during the secondtime period in Block S140; predicting a second pressure gradient of thestressor in the region of the agricultural field during the second timeperiod based on the second pressure of the stressor and the model inBlock S152; and in response to the second pressure gradient predicting athird pressure in a subregion of the agricultural field and exceeding athreshold pressure, generating a prompt to address the stressor inplants occupying the agricultural field proximal the subregion of theagricultural field in Block S160.

As shown in FIGS. 2 and 3 , one variation of the method S100 includes:accessing a first feed of images of a first set of sentinel plants in anagricultural field, the first feed recorded at a first frequency by afixed sensor in Block Silo; accessing a second image of the agriculturalfield recorded by an aerial sensor during a first time period in BlockS114; interpreting a first pressure of a first stressor, in a set ofstressors, in the first set of sentinel plants during the first timeperiod based on a first set of features extracted from a first image, inthe first feed of images, captured during the first time period in BlockS120; interpreting a second pressure of the first stressor in the firstset of sentinel plants during the first time period based on a secondset of features extracted from a region of the second image comprisingthe first set of sentinel plants in Block S122; interpreting a firstpressure gradient of the first stressor in the agricultural field duringthe first time period based on a third set of features extracted fromregions of the second image in Block S124; deriving a model associatingpressure of the first stressor at the first set of sentinel plants withpressure gradient of the first stressor in the agricultural field basedon the second pressure and the first pressure gradient in Block S132;and rectifying the first pressure gradient of the first stressor in theagricultural field during the first time period based on the firstpressure and the model in Block S134.

As shown in FIG. 3 , one variation of the method S100 includes:accessing a first feed of ground-based images recorded by a fixed sensorat a first frequency in Block S110, the fixed sensor facing a first setof sentinel plants in an agricultural field; accessing a second feed ofground-based images in Block S112, of a second set of sentinel plants inthe agricultural field, recorded by a mobile ground sensor at a secondfrequency; accessing a third feed of aerial images in Block S114, of theagricultural field, recorded at a third frequency less than the firstfrequency and the second frequency; estimating a first pressure of astressor in the first set of sentinel plants at a first time based on afirst set of features extracted from the first feed in Block S120;estimating a second pressure of the stressor in the second set ofsentinel plants at a second time based on a second set of featuresextracted from the second feed in Block S122; interpolating pressures inplants between the first set and the second set at the second time basedon the first pressure and the second pressure; calculating a firstpressure gradient in the agricultural field at the first time based on athird set of features extracted from regions of the third feed depictingthe first set, the second set, and a third set of sentinel plants in theagricultural field in Block S124; and rectifying the first pressuregradient in the agricultural field at the first time based on the firststressor in the first set and the second stressor in the second set inBlock S126. The method can further include serving a prompt to anoperator affiliated with the agricultural field to address pressures ofthe stressor in the agricultural field based on the first pressure, thesecond pressure, and the first pressure gradient in Block S160.

2. Applications

Generally, a computer system (e.g., a local computing device, a remoteserver, a computer network) executes Blocks of the method S100: toidentify a stressor present at a sentinel plant based on signals (e.g.,fluorescence in the electromagnetic spectrum) generated by the sentinelplant, which is genetically-modified to signal environmental conditionsadverse to plant health or growth; to interpret presence and/ormagnitude of the stressor at other plants nearby based on signalsgenerated by the sentinel plant; and to selectively generate anddistribute prompts for mitigating the stressor at the sentinel plantand/or at the nearby plants.

More specifically, a sentinel plant can be genetically-modified toinclude a set of promoter-reporter pairs configured to trigger signalgeneration within the sentinel plant in the presence of a particularbiotic and/or abiotic stressor to which the sentinel plant is exposed,such as: a pest; a viral disease; excess or insufficient water; excessheat or cold; and/or nutrient deficiency. An optical device can recordoptical signals generated by the sentinel plant (e.g., in the form ofcolor or multispectral images); and the computer system can extractfeatures (e.g., intensities at particular wavelengths) from theseimages, interpret presence and/or magnitude of a particular stressorexposed to the sentinel plant based on these features, and interpolateor extrapolate health and environmental conditions at other plantsnearby (e.g., non-sentinel plants; other unimaged sentinel plants) basedon presence and/or magnitude of the stressor thus indicated by thesentinel plant.

For example, the computer system can extract intensities of particularwavelengths corresponding to specific compounds (e.g., proteins) in thesentinel plant and interpret a pressure of a particular stressor exposedto the sentinel plant based on intensities of these wavelengths—such asbased on a stored model linking plant stressors to wavelengths ofinterest based on known characteristics of promoter and reporter genesin the sentinel plant—and before such stressors are visually discerniblein the visible spectrum (i.e., with an unaided human eye). The computersystem can also interpolate or extrapolate presence or magnitude ofthese stressors in other plants near this sentinel plant to predictoverall health of a crop or agricultural field.

2.1 Applications: Sentinel Plant Cluster and Fixed Sensor

In one example, the sentinel plant can be genetically engineered toinclude a promoter indicative of a fungal stressor found in corn crops.The promoter can be paired to a red fluorescing reporter, such that thesentinel plant exhibits red fluorescence when exposed to this fungalpressure in excess of a threshold magnitude and/or for more than athreshold period of time. Sentinel plants exhibiting this characteristicmay be planted in clusters throughout an agricultural field planted witha commercial non-sentinel corn crop, such as near a center of theagricultural field. An optical sensor (e.g., a multi-spectral camera)mounted on a pole within the center cluster of sentinel plants cancollect images of the adjacent sentinel plants, such as hourly or daily,and offload these images (e.g., via a computer network) to the computersystem. The computer system can then extract magnitudes (e.g.,intensities) of wavelengths of the red fluorescing reporter from theseimages and implement a stored model to interpret pressure (e.g.,presence and/or magnitude) of the fungal stressor in this center clusterof sentinel plants over time based on magnitudes of these wavelengths.

Based on the interpreted pressure of the fungal stressor, the computersystem can recommend a particular action or set of actions to mitigatethis pressure of the fungal stressor. More specifically, the computersystem can: isolate a subset of actions, in a set of actions, linked tomitigating fungal stressors; and isolate a first action, in the subsetof actions, linked to the pressure of the fungal stressor. For example,the computer system can recommend a first action for mitigating fungalpressures above a threshold fungal pressure and a second action formitigating fungal pressure below the threshold fungal pressure. Further,the computer system can recommend mitigation or treatment techniques forapplication to plants proximal to the center cluster of sentinel plants,such as within a particular distance of the center of the crop based onthe pressure of the fungal stressor. For example, the computer systemcan recommend: treating plants (e.g., sentinel plants and non-sentinelplants) within a first radius from the center cluster with a firstquantity of fungicide at a first frequency; treating plants outside thefirst radius and within a second radius from the center cluster with asecond quantity of fungicide less than the first quantity at the firstfrequency; and treating plants outside the second radius and within athird radius from the second cluster with the second quantity offungicide at a second frequency less than the first frequency. Inanother example, the computer system can recommend treatment ofsurrounding plants based on predicted movement of the fungal pressureacross the crop (e.g., based on a previous pressure of the fungalstressor). In yet another example, the computer system can recommendcollecting samples from soil and plants proximal the fungal pressure inthe cluster of sentinel plants to collect more precise diagnostics withrespect to type and spread of the fungal pressure throughout the cropand determine appropriate treatment.

2.2 Applications: Sentinel Plant Clusters and Ground-Based Mobile Sensor

In the foregoing example, sentinel plants may be planted in otherclusters throughout the agricultural field, such as near each corner ofthe agricultural field. A mobile optical sensor mounted on a truck,tractor, or other farm implement may intermittently capture images ofthese clusters of sentinel plants when driven on an access road alongthis agricultural field, such as multiple times in one day per week. Thecomputer system can: access these images; implement methods andtechniques described above to extract magnitudes of wavelengths of thered fluorescing reporter from these images; implement the storedpressure model to interpret pressures (e.g., presence and/or magnitude)of the fungal stressor in these clusters of sentinel plants based onmagnitudes of these wavelengths; pair these stressor diagnoses for thesecorner clusters with temporally-nearest stressor diagnoses for thecenter cluster; and compile concurrent stressor diagnoses for the cornerand center clusters over time to generate a model that predicts fungalpresence and magnitude at the corner clusters based on fungal presenceand magnitude at the center cluster.

Later, the scan cycle can: implement this model to predict fungalpressures at the corner clusters based on fungal pressure derived from anext image of the center cluster; interpolate fungal pressure throughoutthe crop between the center and corner clusters; and generate prompts orrecommendations for fungal mitigation in all or particular regions ofthe agricultural field.

In one implementation, the mobile optical sensor may capture images ofsentinel plants at different frequencies and at different locationswithin the crop to achieve greater spatial resolution. The mobileoptical sensor may collect these images intermittently andinconsistently (e.g., less temporal resolution). However, the computersystem can leverage data extracted from these images recorded by themobile optical sensor in combination with consistent data extracted fromimages recorded by the fixed sensor over the singular sentinel plantcluster, to expand fungal pressure predictions across the crop. Further,the computer system can converge on a more precise model for predictingpressures across the crop over time based on data extracted from theseimages, such as via incorporations of machine learning algorithms.

2.3 Applications: Sentinel Plant Clusters and Aerial Sensor

In the foregoing example, an aerial optical sensor may intermittentlycapture images of the agricultural field, including each cluster ofsentinel plants, such as biweekly or once per month. From an aerialimage of the agricultural field, the computer system can interpret apressure gradient in the agricultural field and/or a pressure at eachcluster of sentinel plants in the agricultural field. The computersystem can distinguish clusters of sentinel plants from non-sentinelplants in the aerial image, such as by: overlaying a mask over theaerial image configured to obscure regions of the image corresponding tonon-sentinel plants in the agricultural field; detecting a baselinesignal characteristic of sentinel plants but not linked to fungalpressures in subregions of the image corresponding to sentinel-plants inthe agricultural field; and/or matching geotags included in the aerialimage to known GPS locations of sentinel plants in the agriculturalfield. Upon matching subregions of the aerial image corresponding toclusters of sentinel plants, the computer system can derive a pressureof the fungal stressor for each subregion and interpolate betweenpressures at each subregion to interpret a pressure gradient across theagricultural field. Further, the computer system can interpret apressure of the fungal stressor at the cluster of sentinel plants froman image recorded by the fixed sensor during a concurrent time period.Then, based on the location of the fixed sensor corresponding to aparticular subregion of the agricultural field, the computer system canderive a scalar linking pressure of the fungal stressor at thisparticular subregion, as recorded by the fixed sensor, to pressure ofthe fungal stressor at this particular subregion, as recorded by theaerial optical sensor.

The computer system can then rectify (e.g., scale) the pressures at eachsubregion or cluster of sentinel plants according to the scalar. Basedon these updated pressures, the computer system can generate prompts tomitigate fungal pressure in subregions of the crop as needed.

Further, the computer system can derive a gradient model (e.g., scalar)linking pressures of the fungal stressor at the cluster of sentinelplants in the center of the crop, recorded by the fixed sensor, topressures of the stressor at other clusters of sentinel plants (e.g.,gradient of the agricultural field). At a later time, the computersystem can: access an image of the cluster of sentinel plants recordedby the fixed sensor; interpret a pressure of the fungal stressor at thecluster of sentinel plants based on features extracted from the image;and predict a pressure of the fungal stressor at each subregion of theagricultural field based on the pressure of the fungal stressor at thecluster of sentinel plants and the gradient model.

3. Terms

As described above, a “sentinel plant” is referred to herein as a plantconfigured to signal presence of a particular stressor or set ofstressors within and/or at the plant. A sentinel plant can begenetically-modified to include a set of promoter-reporter pairs (e.g.,one promoter-reporter pair, three promoter-reporter pairs) configured totrigger generation of a detectable signal or signals by the sentinelplant in the presence of a particular stressor or set of stressors. Forexample, a sentinel plant can be genetically-modified to include a firstpromoter-reporter pair configured to trigger generation of a redfluorescence signal by the sentinel plant in the presence of fungi.Thus, the sentinel plant can generate a detectable signal that, whendetected, may alert a user (e.g., a farmer, an agronomist, a botanist)associated with the sentinel plant of a stressor or stressors present.Further, a sentinel plant of a first plant type can be configured tosignal presence of stressors in plants of the first type and/or of adifferent type. For example, a sentinel corn plant can be configured tosignal presence of stressors in corn plants. In another example, asentinel tomato plant can be configured to signal presence of stressorsin potato plants.

In one implementation, a sentinel plant can be monitored for thepresence of stressors (e.g., pests, diseases, dehydration) in other(non-sentinel) plants. Generally, a small quantity of sentinel plantscan be monitored to extract insights into a larger population of plants(e.g., in crops). For example, a cluster of sentinel plants can beplanted along an outside edge of a crop of plants and monitored for thepresence of pests to inform a user (e.g., a farmer, an agronomist, abotanist) associated with the crop if and/or when a population of pestshas entered the crop along this outside edge. In another example, asentinel plant of a first plant type (e.g., tomatoes) can be grown in agreenhouse setting (e.g., glass roof or factory farm), located in aparticular region, and monitored for the presence of stressors (e.g.,dehydration, disease, pest) indicative of plant health. A userassociated with the greenhouse setting may extract insights fromstressors present at the sentinel plant to inform planting and/ortreatment of other plants (e.g., in a crop) of the same plant type whengrown in the particular region.

As described above, a “stressor” is referred to herein as a type ofabiotic and/or biotic stress that may negatively affect plant health,such as pest, disease, water, heat, and/or nutrient stresses ordeficiencies. For example, a plant may experience an insect stressorcorresponding to presence of an insect or insect population at the plantthat may hinder plant growth and/or health.

As described above, a “pressure” is referred to herein as a measurableand/or detectable presence of a particular stressor and/or set ofstressors in plants (e.g., in a cluster of sentinel plants, in a crop ofplants). For example, the computer system can detect an insect stressorat a cluster of sentinel plants and—based on features extracted fromimages of the cluster of sentinel plants—estimate an insect pressure(e.g., measurable presence, distribution, magnitude) at this cluster.Thus, a pressure represents a measurable presence of a particularstressor.

As described above, a “pressure gradient” is referred to herein as adistribution of pressures of a stressor (or stressors) across multiplesentinel plants and/or sets (or clusters) of sentinel plants in anagricultural field. For example, a user may initially distribute threesets of sentinel plants within an agricultural field. Later, thecomputer system can access images of the agricultural field, recorded byan aerial sensor (e.g., a satellite), depicting the three sets ofsentinel plants. Based on features extracted from regions of the imagedepicting each set of sentinel plants, the computer system can interpreta pressure gradient of a stressor in the agricultural field. Morespecifically, the computer system can: interpret a first pressure of thestressor in a first set of sentinel plants based on features extractedfrom a first region of the image depicting the first set of sentinelplants; interpret a second pressure of the stressor in a second set ofsentinel plants based on features extracted from a second region of theimage depicting the second set of sentinel plants; interpret a thirdpressure of the stressor in a third set of sentinel plants based onfeatures extracted from a third region of the image depicting the thirdset of sentinel plants; and interpret the pressure gradient of thestressor in the agricultural field based on the first pressure, thesecond pressure, and the third pressure. Based on this pressuregradient, the computer system can interpret pressures of the stressor atvarious locations within the agricultural field (e.g., viainterpolation).

As described above, a “user” is referred to herein as a personassociated with an agricultural environment including sentinel plants,such as an agricultural field, a crop of plants, a greenhouse, anarboretum, or a laboratory. For example, a user may refer to a farmerassociated with a particular agricultural field. In another example, auser may refer to an agronomist associated with a particular crop ofplants. In another example, a user may refer to a scientist studying ordeveloping sentinel plants and/or treatments of stressors in sentinelplants and non-sentinel plants.

4. Promoter and Reporter Pairs

A network of sentinel plants can be deployed to an agricultural field tocommunicate (e.g., visually, thermally, chemically) biotic and abioticstressors in nearby crops, such as to a farmer, field operator, oragronomist. In particular, a sentinel plant can experience, react, anddeteriorate in presence of certain plant stressors in the same orsimilar measures as comparable non-sentinel plants planted in the cropwhen exposed to these plant stressors and stressors. Therefore, thesentinel plant may function as an accurate sensor and predictor ofdisease and/or stressors in these nearby crops. For example, sentinelplants can be deployed to an agricultural field and planted with othernon-sentinel plants—such as in clusters of sentinel plants surrounded bynon-sentinel plants—in order to detect, measure, and communicate certainstressors in these sentinel plants, which may then be interpolated orextrapolated to stressors in nearby non-sentinel plants.

To generate a sentinel plant, plant cells can be genetically-modified tocouple a known reporter gene with a certain biological process.Molecular genetic techniques can be implemented to associate anexpression of the reporter gene with certain biological stresses andtraits. Therefore, the reporter gene can act as a signal of a biologicalstress or trait in the plant cells. For example, the sentinel plant canbe modified to fluoresce (i.e., absorb photons at one frequency and emitphotons at a different frequency) in the presence of (and proportionalto) a disease or stressor. In this example, the sentinel plant can bemodified to fluoresce in the presence of one or more disease orstressors, such as: fungi, bacteria, nematode, parasites, viruses,insects, heat, water stress, nutrient stress, phytoplasmal disease, etc.In another example, the sentinel plant can be modified to signalpresence of a stressor via bioluminescence of the sentinel plant. In yetanother example, the sentinel plant can be modified to signal presenceof a stressor via a pigmentation change of the sentinel plant.

Plant cells can be genetically-modified to include promoter and reporterpairs that indicate presence of certain stressors in a plant or crop ofplants. A promoter includes genetic regulatory elements that driveexpression of mRNA at a specific time and place that is subsequentlytranslated into a functional protein. Promoter activity isrepresentative of native biological processes that occur when aparticular stress is present in the plant. To detect presence of thesestressors, a known reporter gene that expresses a certain signal can becoupled to the promoter of choice. Therefore, when the plant's cellsexpress the promoter associated with a certain stressor, the reportertagged to the promoter is also expressed and thus detectable. Somefluorescent signals exist naturally in plants without geneticmodification. These signals can be enhanced by selective breeding and/orother plant selection techniques. Each of these reporter genes canproduce an optical signal that is distinguishable from the plant itself.A combination of reporter genes can be used as well, to indicate variousplant stressors present in the plant or crop.

The promoter and reporter pairs can be implemented by tagging onereporter to one promoter. For example, if a red fluorescent protein istagged to a promoter gene indicative of water stress in a sentinelplant, the promoter gene and therefore the red fluorescent protein canexpress in the plant cells when the water level in the plant cells fallsbelow a minimum water potential. A computer system (e.g., a computernetwork, a remote server) can: access an image of a field containing thesentinel plant collected by various fixed or mobile, local or remotesensors (e.g., a fixed camera mounted to a pole in a field, a smartphoneor tablet, a sensor mounted to a truck or 4×4, a sensor mounted to adrone or crop duster, a sensor mounted on a drone or plane, a cameraintegrated into a satellite); extract intensities of target wavelengthsof red fluorescence—produced by a reporter protein in the sentinel plantin the presence of a water stressor—from this image; estimate amagnitude of a water stressor in this plant based on the intensity ofthe target wavelength of red fluorescence in this image. In response tothis estimated pressure of the water stressor exceeding a thresholdwater pressure, the computer system can alert a field operator toaddress irrigation (e.g., under-irrigations) in a region of the fieldoccupied by the sentinel plant. In this example, the computer systemcan: repeat this process to extract intensities of target wavelengths ofred fluorescence from other regions of this same image or otherconcurrent images depicting other individual or clustered instances ofthis sentinel plant planted in other regions of this field; estimatepressure of the water stressor in these other regions of the field basedon these intensities of the target wavelength of red fluorescenceextracted from other regions of this image and/or from other concurrentimages of the field; and interpolate or extrapolate a water pressuregradient across the entire field based on locations and pressures ofwater stressors indicated in these sentinel plants distributedthroughout the field. Accordingly, the computer system can notify afield operator to address irrigation across the entire field or intargeted regions of the field based on this water pressure gradient.Furthermore, the computer system can: repeat this process over time toestimate water pressures or water pressure gradients in a region oracross the entirety of the field; extrapolate future water pressures inthe field based on the region-specific or field-wide water pressuresthus derived from sequential images of sentinel plants occupying thefield; and then prompt the field operator to preemptively addresspredicted future water pressure changes in the field well before achange in water pressure (substantively) affects crop yield from thisfield.

In one variation, multiple promoters can be tagged to one reporter suchthat the sentinel plant outputs a signal for a particular stressor overan extended duration of time. For example, a set of three promoterslinked to water stress can be tagged with the red fluorescence proteinreporter. At an initial time, presence of the first promoter can triggerthe expression of the red fluorescence protein in response to a certainwater pressure. At a second time, as the signal produced by the firstpromoter decreases, presence of the second promoter can trigger thecontinued expression of the red fluorescence. And again, at a thirdtime, a third promoter can trigger the expression of the redfluorescence in the plant. Therefore, genetic engineering techniques canbe implemented to string together multiple promoter genes and tag thisstring of promoters with a reporter gene for identifying which promotergenes are expressed in the plant, thus extending the detection window.

In one implementation, the sentinel plant can be configured to include afirst quantity of promoters and a second quantity of reporters less thanthe first quantity of promoters. For example, expression of the redfluorescent protein can signal presence of a certain water pressure, andexpression of the yellow fluorescent protein can signal presence of acertain heat pressure. However, the expression of both the redfluorescent protein and the yellow fluorescent protein can signal eitherpresence of both a certain water pressure and heat pressure, or presenceof a third pressure, such as a certain insect pressure. Therefore,fluorescence of the sentinel plant can be combined with knowledge ofdisease frequency, common disease locations, and common disease times toisolate a particular plant stressor present in the agricultural field.In another example, a first, second, and third fluorescing compound areeach coupled to a first, second, and third biological process,respectively. Additionally, a fourth biological process is coupled tothe first and second fluorescing compound; a fifth biological process iscoupled to the second and third fluorescing compound, a sixth biologicalprocess is coupled to the first and third fluorescing compound; and aseventh biological process is coupled to the first, second, and thirdfluorescing compound. In this example, the detection of all threefluorescing compounds in a plant can signal each of the following:activation of the sixth biological process; activation of the first,second, and third biological process; activation of the first and fifthbiological process; activation of the fourth and third biologicalprocess; activation of the sixth and second biological process. Thesebiological processes can be distinguished to enable detection ofdifferent processes occurring in these plant cells —and thereforedifferent stressors present at the plant. For example, the computersystem can prompt the crop manager to treat all possible diseases or aspecific disease that can be catastrophic if not treated quickly. Inanother example, a farmer or agronomist may retrieve a sample from theplant and test for each possible disease to initiate an appropriatecourse of action.

Similarly, plant cells can be genetically-modified to includecombinatorial reporters that present different signals responsive todifferent stressors and/or pressures. The computer system can thenleverage a model to interpret these signals, including deriving moreinformation than the sum of this set of reporters, such as: a type offungus in addition to presence of a fungal pressure; or proportion ofwater stress to heat stress.

4.1 Sentinel Plant

As shown in FIG. 5 , a sentinel plant includes a first promoter-reporterpair including: a first promoter that activates in the presence of afirst stressor at the sentinel plant; and a first reporter coupled tothe first promoter and configured to exhibit a first signal in theelectromagnetic spectrum in response to activation of the first promoterby the first stressor.

In one variation, as shown in FIG. 5 , the sentinel plant furtherincludes a second promoter-reporter pair including: a second promoterthat activates in the presence of a second stressor at the sentinelplant; a second reporter coupled to the second promoter and configuredto exhibit a second signal in the electromagnetic spectrum in responseto activation of the first promoter by the second stressor, the secondsignal different from the first signal.

In one variation, the sentinel plant further includes a third promoterthat activates in the presence of a third stressor at the sentinelplant, the first reporter and the second reporter both coupled to thethird promoter and configured to exhibit a third signal in theelectromagnetic spectrum in response to activation of the third promoterby the third stressor, the third signal different from the first signaland the second signal.

One variation of the sentinel plant includes a first promoter-reporterpair including: a first promoter configured to activate in the presenceof a first stressor within a first magnitude range at the sentinelplant; and a first reporter coupled to the first promoter and configuredto exhibit a first signal in the electromagnetic spectrum in response toactivation of the first promoter by the first stressor. In thisvariation, the sentinel plant also includes a second promoter-reporterpair including: a second promoter configured to activate in the presenceof the first stressor within a second magnitude greater than the firstmagnitude range at the sentinel plant; and a second reporter coupled tothe second promoter and configured to exhibit a second signal in theelectromagnetic spectrum in response to activation of the secondpromoter by the second stressor.

Another variation of the sentinel plant includes: a first promoter thatactivates at a first time over a first duration in response to a firststressor presence in the sentinel plant; a second promoter thatactivates at a second time for a second duration in response to thefirst stressor presence in the sentinel plant, the second timesucceeding the first and preceding the termination of the firstduration; and a reporter coupled to the first and second promoter that,in response to activation of the first promoter, exhibits a first signalover the first duration for detection of the first stressor; and, inresponse to activation of the second promoter, exhibits a second signalover the second duration for detection of the first stressor.

5. Detection

The computer system can detect and interpret signals generated bysentinel plants by extracting features from images of sensor plants thatcorrelate to presence of particular stressors at the sentinel plants.

In one implementation, the computer system can access digital images(e.g., spectral images) of a sentinel plant(s) and/or plant canopy(e.g., sentinel plants and surrounding plants) captured by an opticalsensor (e.g., a multispectral or hyperspectral imaging device) deployedat the sentinel plant(s) and/or plant canopy. For example, as shown inFIG. 7 , the optical sensor can include: an optomechanical fore opticthat enables measurement of fluorescent and non-fluorescent targets; anda digital spectrometer or digital camera that records images through theoptomechanical fore optic. The computer system can thus access imagesrecorded by the optical sensor and process these images according to themethod S100 to detect reporter signals and interpret stressors presentin these plants. More specifically, in this example, the computer systemcan: access images (e.g., spectral) of sentinel plants recorded by thedigital spectrometer; extract wavelengths of the compounds of interestfrom these images; and identify stressors present at the sentinel plantsbased on these wavelengths.

The computer system can access images of sentinel plants captured by anoptical sensor, such as from a handheld camera, a handheld spectrometer,a mobile phone, a satellite, or from any other device that includes ahigh-resolution spectrometer, includes band-specific filters, or isotherwise configured to detect wavelengths of electromagnetic radiationfluorescence, luminescence, or passed by the sentinel plant in thepresence of a particular stressor.

The computer system can implement different instrumentation depending onthe compound of interest, as the wavelengths of different compounds areeach best observed under different conditions and may require distinctmodes of detection. For example, the computer system can: access images,captured by a handheld spectrometer, of sentinel plants configured toemit red fluorescence in the presence of a stressor; and access images,captured by a handheld camera, of sentinel plants configured to exhibita change in pigmentation in the presence of a stressor.

The computer system can access images of sentinel plants collected atparticular times of day and/or time intervals so as to maximizedetectability of signals generated by sentinel plants. For example, fora sentinel plant configured to produce a bioluminescent signal in thepresence of a particular stressor or stressors, the computer system canaccess images of the sentinel plant collected at night when othersignals generated by the sentinel plant and its surroundings areminimized.

5.1 Active/Passive Detection

The computer system can detect and interpret pressures of stressors insentinel plants via active and/or passive modes of detection. Forexample, the computer system can implement passive detection to detect asignal generated by sentinel plants —without excitation of the sentinelplants—in the presence of a stressor or stressors. Alternatively, thecomputer system can implement active detection to detect a signalgenerated by sentinel plants—in response to excitation of the sentinelplant (e.g., via external illumination)—in the presence of a stressor orstressors. More specifically, the computer system can implement adetection method in which sentinel plants are illuminated in anoscillating light for excitation such that the response to thatillumination can be isolated.

In one variation, the computer system detects solar-induced fluorescentsignals generated by sentinel plants via narrow-wavelength measurementsnear dark spectral features in incident solar radiation. Narrow bandtechniques associated with Fraunhofer lines (from absorption in thesolar atmosphere) and Telluric lines (which originate from absorption ofmolecules in Earth's atmosphere) enable measurement of the opticalsignals in daylight, without implementing external illumination.Implementing this measurement technique allows for both specificity andaccuracy of measuring small, obscure signals, as well as the ability tocollect measurements both on the ground and airborne. Therefore, it ispossible to collect images of the sentinel plants from a large range ofdistances. The computer system can detect these solar-inducedfluorescent signals and extract insights into pressures of stressors atsentinel plants generating these signals. For example, as shown in FIGS.5 and 6 , the computer system can: access a first feed of spectralimages captured by a first optical spectrometer; interpret a firstpressure of a stressor in the first set of sentinel plants based onsolar-induced fluorescence measurements extracted from a first image inthe first feed of images; access a reporter model linking solar-inducedfluorescence measurements extracted from spectral images to pressures ofstressors for sentinel plants; and interpret a first pressure in thefirst set of sentinel plants based on a first solar-induced fluorescencemeasurement extracted from the first image.

5.2 Single-Plant Fixed Sensor

In one implementation, the computer system can access data from a singlesentinel plant, in an agricultural field or greenhouse, recorded by afixed sensor. For example, the computer system can access imagescollected by an optical sensor configured to install (e.g., clamp) ontoa leaf or stalk of the sentinel plant and to capture close-range imagesof fluorescing surfaces on the sentinel plant at a high frequency (e.g.,once per minute, once per hour). In these examples, the computer systemcan upload images to a remote database via a cellular network, or imagescan be downloaded to a mobile device or vehicle via a local ad hocwireless network when a mobile device or vehicle is nearby, and thenuploaded from the mobile device or vehicle to the remote database.

5.3 Fixed Cluster Sensor

In one implementation, the computer system can access images of a set(e.g., cluster) of sentinel plants collected by a fixed optical sensorfacing the set of sentinel plants and installed (e.g., mounted) in theagricultural field. For example, the computer system can access imagesof a cluster of sentinel plants in an agricultural field recorded by anoptical sensor mounted to a boom or column located in a center of thecluster of sentinel plants to capture close-range images of fluorescingsurfaces on sentinel plants in the cluster of sentinel plants at a highfrequency (e.g., once per hour, once per day). The computer system canextract insights from these close-range images of the cluster ofsentinel plants to interpret pressures of a particular stressor(s) inthis cluster of sentinel plants. Further, by interpreting pressures inthe cluster of sentinel plants from images recorded by a fixed sensorlocated at the cluster, the computer system can extract insights intopressures in a subregion of the agricultural field including the clusteras well as adjacent subregions.

5.4 Handheld Sensor

In another implementation, a farmer may manually collect data forsentinel plants on a handheld device. For example, the computer systemcan access images of a first cluster of sentinel plant along an edge ofan agricultural field collected by a mobile device (e.g., a smartphone)operated by a farmer associated with the agricultural field to captureclose-range images of the cluster of sentinel plants at a lowerfrequency (e.g., once per week, biweekly). Additionally oralternatively, the computer system can access close-range images of asingle sentinel plant in the cluster of sentinel plants. In thisimplementation, the computer system can upload images to a remotedatabase via a cellular network or automatically upload images via anative or web-based agricultural application executing on the handhelddevice. The computer system can interpret pressures in this cluster ofsentinel plants and/or single sentinel plant directly from featuresextracted from these close-range images to generate a high-resolution,short-interval time series representation of the health of this clusterof sentinel plants and/or single sentinel plant.

5.5 Ground-Based Mobile Imaging

Alternatively, the computer system can implement ground-based mobileimaging to extract insights into the health of sentinel plants andcluster of sentinel plants by collecting images from optical sensorsinstalled in manned or unmanned vehicles. For example, the computersystem can access images of a cluster of sentinel plants collected by anoptical sensor configured to install (e.g., mount) into a bed of a truckoperated by a farmer associated with an agricultural field including thecluster of sentinel plants. In the example, the farmer may drive thetruck along an edge of the agricultural field in order to capture imagesof the cluster of sentinel plants as the truck moves along the edge ofthe agricultural field. The computer system can then upload these imagesto the remote database, timestamped and georeferenced, and access theseimages upon upload or at a later time.

5.6 Aerial Imaging

In one implementation, the computer system can access images of acluster of sentinel plants, multiple clusters of sentinel plants, and/ora crop of sentinel plants recorded by an aerial sensor configured tocapture images of sentinel plants. For example, the computer system canaccess images of a crop of sentinel plants collected by an opticalsensor configured to install (e.g., mount) onto a drone operated by anagronomist associated with the crop. Alternatively, in a crop ofnon-sentinel plants with clusters of sentinel plants, the farmer mayoperate a drone or dispatch an autonomous drone to scan regions of thecrop where sentinel plant clusters are located to collect images ofthese sentinel plants.

In another implementation, the computer system can access images of acluster of sentinel plants, multiple clusters of sentinel plants, and/ora crop of sentinel plants recorded by an aerial sensor (e.g.,long-duration, high-altitude UAVs or a satellites such as OCO-2 orGOSAT) configured to capture long-range images of sentinel plants. Forexample, the computer system can access images collected by a satellitesensor configured to collect long-range images of sentinel plants at alow frequency (e.g., once per week, biweekly, once per month). Inanother example, the computer system can access images collected by acommercial satellite sensor configured to collect long-range images ofsentinel plants at relatively higher frequencies (e.g., once per day,multiple times per week).

The computer system can implement any combination of these methods ofdata collection (e.g., instrumentation, frequency, range) to collecthigh-quality data that enable rapid, targeted responses to certain plantstressors and therefore increase yield of the non-sentinel plants nearbyin the same agricultural field. For example, the computer system canaccess high-resolution images recorded by a high-resolution opticalsensor (e.g., a RGB camera, a multispectral camera or spectrometer, athermal or IR camera) mounted to a pole located in a center of a firstcluster of sentinel plants in a crop and configured to capturehigh-resolution images of the sentinel plants at a high frequency (e.g.,three times per day) each day and upload these images to a remotedatabase. The computer system can extract features (e.g., intensity atparticular wavelengths) from these high-resolution images to interpretpressures of a stressor at the first cluster of sentinel plants.Additionally, the computer system can access low-resolution imagesrecorded by a satellite sensor configured to capture low-resolutionimages of the entire crop, including multiple sentinel plant clusters,at a low frequency (e.g., once per two-week interval). The computersystem can extract features (e.g., intensity at particular wavelengths)from these low-resolution images to interpret pressures of the stressorat each cluster of sentinel plants in the crop. The computer system canderive a model linking pressures of the stressor at the first cluster ofsentinel plants to the pressures of the stressor at other clusters inthe crop based on the daily behavior of the first cluster and thebiweekly behavior of all clusters of sentinel plants in the crop; andinterpolate behavior of the crop as a whole in regions with or withoutsentinel plants.

6. Imaging Frequency

The computer system can access images of sentinel plants captured at setintervals or particular times of day in order to increase likelihood ofdetection of signals and to detect pressures of stressors in sensorplants and crops including sensor plants at early stages before thesepressures expand in magnitude or negatively affect crop yield. Forexample, the computer system can access images of sensor plants in acrop recorded by optical sensors to monitor pressures of stressorsindicative of plant health and to prompt users (e.g., a farmer)associated with the crop to mitigate these pressures once detected(e.g., above a threshold pressure). Alternatively, a user manuallymonitoring a crop may not visibly see or detect pressures of stressorsin the crop until after a pressure has significantly damaged plants inthe crop. Thus, the computer system can lower risk or probability ofpressures spreading throughout a crop and across crops into otherfields, and increase overall crop yield. Further, sentinel plants can beconfigured to output signals of relatively large magnitudes (e.g.,greater intensity) responsive to pressures of stressors at relativelylow magnitudes. Sentinel plants can include promoters configured toactivate within hours of an initial infection or deficiency at thesensor plant. The computer system can then detect a signal generatedfrom activation of the promoter in the sensor plant. Based on earlydetection of the signal, the computer system can recommend a minimaltreatment to mitigate a pressure in the sensor plant.

The computer system can regularly monitor a set of sentinel plants atset frequencies such that pressures of stressors in sentinel plants aredetected early while limiting cost and effort by users (e.g., farmers,agronomists) associated with an agricultural field including the set ofsentinel plants. For example, the computer system can: access a feed ofimages of a set of sentinel plants in an agricultural field recorded ata set frequency (e.g., twice per day, daily, weekly); interpret apressure of a stressor in the set of sentinel plants based on featuresextracted from a first image, in the first feed of images; and, inresponse to the pressure exceeding a threshold pressure, generate aprompt to a user associated with the agricultural field to address thestressor in plants occupying the agricultural field proximal the set ofsentinel plants. In this example, if the pressure falls below thethreshold pressure, the computer system can continue accessing images,in the first feed of images, at the set frequency, to continuemonitoring the pressure of the stressor in the set of sentinel plants.Additionally and/or alternatively, the computer system can generate aprompt alerting the user of the pressure of the stressor. Thus, thecomputer system enables the user to regularly monitor health of sentinelplants and/or plants in agricultural fields associated with the userwhile minimizing physical travel to agricultural fields includingsentinel plants, treating of sentinel plants, and/or testing of sentinelplant health by the user.

In one implementation, the computer system implements both highfrequency and lower frequency measurements in order to more preciselyinterpret and predict pressures of stressors in sentinel plants andagricultural fields including sentinel plants. In this implementation,the computer system can combine high-resolution, short-interval timeseries representation of the health of this sentinel plant with featuresextracted from low-frequency, wider field-of-view images of clusters ofplants or a whole field containing this sentinel plant to predict thehealth of multiple or all plants in this field. For example, thecomputer system can access a first feed of images recorded at a firstfrequency (e.g., twice per day, once per day, biweekly) by a fixedsensor facing a first set of sentinel plants in an agricultural field.Additionally, the computer system can access a second feed of images, ofa region of the agricultural field including the first set of sentinelplants, recorded by a mobile sensor (e.g., deployed by a user associatedwith the agricultural field) at a second frequency less than the firstfrequency (e.g., weekly, every two weeks). From images in these feeds,the computer system can derive a model linking features extracted fromimages in the first feed of images to pressures of stressors at both thefirst set of sentinel plants and in the region of the agriculturalfield. Thus, the computer system can predict pressures across the regionof the agricultural field at the first frequency based on featuresextracted from images in the first feed. The computer system canregularly confirm and/or rectify the model based on features extractedfrom images in the second feed at the second frequency.

7. Tagging Sentinel Plants

The computer system can extract features (e.g., intensities atparticular wavelengths) from images of a sentinel plant(s), a cluster ofsentinel plant(s), and/or an agricultural field including sentinelplants to interpret pressures of stressors in these sentinel plants. Inorder to extract these features, the computer system can distinguishsentinel plants from non-sentinel plants in these images.

In one implementation, the computer system can identify locations inagricultural fields that include sentinel plants and extract featuresfrom images or regions of images corresponding to these locations. Forexample, the computer system can access georeferenced images of clustersof sentinel plants in an agricultural field recorded by a ground-basedmobile sensor. The computer system can: access a position andorientation of the ground-based mobile sensor when the images werecaptured; access a set of GPS coordinates corresponding to locations ofclusters of sentinel plants in the agricultural field; and identifyclusters of sentinel plants in the images based on the position andorientation of the ground-based mobile sensor and the GPS coordinates ofthe clusters of sentinel plants.

In another implementation, the computer system can identify sentinelplants in images of sentinel plants and non-sentinel plants based on abaseline signal generated only by sentinel plants. For example, sentinelplants can be configured to generate a baseline signal within a firstwavelength band at which non-sentinel plants do not generate any signal.Further, these sentinel plants can be configured to generate a signalwithin a second wavelength band responsive to pressures of a stressor atthe sentinel plant, the second wavelength band distinct from the firstwavelength band. Thus, the computer system can check subregions ofimages of clusters of sentinel plants or crops including sentinel plantsfor this baseline signal within the first wavelength band, to identifyregions of the images including sentinel plants and/or clusters ofsentinel plants.

In another implementation, the computer system can identify sentinelplants in aerial images of crops (e.g., sentinel plants and non-sentinelplants) by overlaying images with a mask configured to hide non-sentinelplants and highlight sentinel plants. For example, the computer systemcan generate a mask for a particular agricultural field including fiveclusters of sentinel plants distributed throughout the agriculturalfield, the mask defining an opaque layer including five transparentregions corresponding to the five clusters. The computer system canthen: overlay the mask over an image of the crop captured by an aerialsensor; apply null pixel values to regions of the crop covered by theopaque layer; and extract features (e.g., intensity measurements) fromthe five transparent regions corresponding to the five clusters ofsentinel plants in the crop.

7.1 Feature Extraction

As shown in FIGS. 4, 5, and 6 , the computer system can extract featuresfrom these images of sentinel plants to interpret pressures in sentinelplants. For example, the computer system can: access a first feed ofimages of a first set of sentinel plants in an agricultural field; andinterpret a first pressure of a stressor in the first set of sentinelplants based on a first set of features extracted from a first image, inthe first feed of images. More specifically, the computer system can:extract a first feature, in the first set of features, from the firstimage, the first feature corresponding to a first pixel of the firstimage; extract a second feature, in the set of features, from the firstimage, the second feature corresponding to a second pixel of the firstimage; and estimate a representative feature based on a combination ofthe first feature and the second feature; access a reporter modellinking features extracted from images in the first feed to pressures ofthe first stressor at the first set of sentinel plants; and interpretthe first pressure of the first stressor in the first set of sentinelplants based on the representative feature and the reporter model. Thus,based on features extracted from images collected by the optical sensor,the computer system can interpret a pressure of a stressor at a sentinelplant or sentinel plants based on a reporter model linkingcharacteristics (e.g., intensity of wavelength) to a particular stressor(e.g., insects, heat, fungi) and/or pressure of the particular stressor.

8. Sentinel Plant Distribution

In one implementation, each sentinel plant type for a particular crop isconfigured to produce a signal responsive to one plant stressor—that is,one sentinel plant type includes one promoter-reporter pair configuredto produce a signal for one type of stressor. For example, a firstsentinel plant type for a particular crop (e.g., corn) includes apromoter-reporter pair configured to output a signal responsive to afungi pressure; and a second sentinel plant type for this particularcrop includes a different promoter-reporter pair configured to output asignal responsive to an insect pressure.

In another implementation, promoter-reporter pairs configured to outputsignals for multiple distinct stressors are integrated into one sentinelplant type for a particular crop. For example, one sentinel plant typefor a particular crop contains promoter-reporter pairs configured toproduce: a luminescent signal responsive to fungi pressure; apigmentation change responsive to insect pressure; and a redfluorescence signal responsive to phosphorus deficiency. Thus, one plantor cluster of plants of this sentinel plant type can be sensed to detectmultiple discrete pressures.

In one variation, the sentinel plants can be planted in clusters—ratherthan mixed with the non-sentinel plant seeds—when a field is planted. Inparticular, rather than mixing seeds of a sentinel plant for aparticular stressor with the non-sentinel seeds of the same or similarplant type prior to planting, and these sentinel plants seeds can beplanted in clusters in designated sentinel plant seed regions in thefield, such as in specific crop rows (e.g., every 50^(th) crop row) orin target segments of crop rows (e.g., three-row-wide, three-meter-longclusters with a minimum of 20 crop rows or 20 meters between adjacentclusters of sentinel plants). Thus, by clustering these sentinel plants,adjacent or surrounded by non-sentinel plants in the same field,stress-related signals produced by these sentinel plants may exhibithigh contrast with adjacent non-sentinel plants and thus yield a highsignal-to-noise ratio for presence of the particular stressor in thefield. For example, by planting multiple instances of the sentinel plantin a small region of the field, a red fluorescing reporter output bythese sentinel plants may be more easily distinguished against anon-fluorescent background of adjacent non-sentinel plants. Similarly,if multiple sentinel plants are planted in one row in the field, thiscluster of sentinel plants can produce a cumulative signal—indicatingpresence of an insect pressure as the insect pressure migrates across acrop—characterized by a greater signal-to-noise ratio than a lonesentinel plant in this row, and this cluster of sentinel plants may alsoyield greater spatial information regarding direction and scope of theinsect pressure moving across the field than a lone sentinel plant inthis row.

Clusters of sentinel plants can be planted with non-sentinel plant cropsin the field, wherein clusters of sentinel plants contain at least onesentinel plants for each stressor or in which each sentinel plantincludes a promoter for each plant stressor. For example, batches ofsentinel plant seeds—including at least one seed containing a promoterfor at least one stressor—can be planted in clusters in a field withother non-sentinel plants. In another implementation, clusters ofsentinel plant seeds are grouped by promoter. In this implementation, afirst cluster of water pressure sensing seeds, a second cluster of fungipressure sensing seeds, and a third cluster of insect pressure sensingseeds are planted in discrete groups in the field. In thisimplementation in which the sentinel plant seeds containing the samereporter are planted together in clusters, these clusters may outputstronger, higher-amplitude, lower noise signals that are more easilyidentifiable by a fixed, local-mobile, or remote sensor when acorresponding pressure is present in the field.

The location of sentinel plant clusters can also be selected to enabledetection of certain plant stressors with greater accuracy and/orreduced noise. In one example in which an agronomist or a farmer isphysically present to collect stressor data from a field—such as via asensor mounted on a vehicle or via a handheld device—the clusters ofsentinel plants can be planted near the edges of the crop to enablequick access for the farmer. In this example, because sentinel plantclusters are located near the edge of a crop, a farmer may collectsamples from these sentinel plants and test these samples directly forplant stressors in order to verify pressures indicated by reporters inthese sentinel plant clusters. In another example, sentinel plants areplanted in the center of the crop to increase proximity to each plant inthe crop, and therefore potentially increase sensing capabilities or thelikelihood of detecting a disease migrating across the crop.

In yet another example, if a farmer's crop shares an edge with anotherfarmer's crop, it might be desirable to plant a row of insect pressuresentinel plants along the shared edge in order to quickly detect amigrating insect population immediately as they enter the crop. Inanother example, if there is a lower elevation portion of a crop, acluster of water pressure sentinel plants may be planted in this area,in order to detect when this area is collecting an excess amount ofwater. A cluster can also be planted at the highest elevation portion ofthe crop, where plant dehydration might be prevalent.

In the implementation described above in which sentinel plants aredistributed in clusters throughout a field, the sentinel plants can beidentified and distinguishable from the non-sentinel plants in order toimprove efficiency of data collection. For example, if a farmer is usinga handheld device to collect images of the clusters on a weekly basis, amarker can be placed in the field such that the cluster is easilylocated. In another example, where satellite images are used to collectimages of crops, the coordinate location of clusters can be obtained inorder to collect wavelength measurements of the sentinel plants.

In another implementation, the sentinel plant seeds are mixed with thenon-sentinel plant seeds and also planted together in clusters. Theclusters of solely sentinel plant seeds can be evenly distributedthroughout a crop or in optimized locations. The sentinel plant seedscan be mixed with the non-sentinel plant seeds such that the mixed seedsare approximately 2 percent sentinel plant seeds. The clusters ofsentinel plants can be analyzed more frequently, such as by a drone thatscans the clusters of sentinel plants each day to collect aerial images.A satellite can collect images of the crop as a whole less frequently,collecting data for both the clusters of sentinel plants and theindividual sentinel plants mixed in with the rest of the crop. Thehealth of the entire crop or agricultural field can be predicted by thecomputer system based on the timestamped and georeferenced images of thesentinel plants.

In one implementation, sentinel plants can be transplanted as seedlingsinto a crop. For example, a sentinel strawberry plant may be initiallytransplanted as a seedling to a field of strawberry plants. In anotherimplementation, sentinel plants can be sown as seeds into a crop. Forexample, a sentinel soybean plant may be initially sown as a seed into acrop of soybean plants. In yet another implementation, sentinel plantscan be grafted onto existing perennial crops. For example, a sentinelgrape scion sensor can be grafted to a grape producing vine.

8.1 Variation: Sterile Sentinel Plants

Sentinel plants can be genetically-modified to be sterile, ornon-flowering. Sterile sentinel plants can be grown in GMO or non-GMOcrops, as they will not reproduce. A small percentage of a field can beplanted with the sterile sentinel plant seeds, while the rest of thecrop is planted with standard non-sentinel plant seeds. For example, afarmer planting a crop of corn may plant 2-5% of a crop as thegenetically-modified sterile sensing corn plants and the remaining95-98% of the crop as the standard non-sterile corn plants. Beforeplanting, the different sentinel plant types can be mixed together at anappropriate ratio such that the sterile seeds are approximately 2-5% ofthe total seeds planted. When the crops grow, the sterile plants will berandomly distributed throughout the crop to produce an approximatelyeven distribution of sterile plants in the crop. In this example, eachplant stressor can be detected in each area of the crop either by havingone sentinel plant type that contains all the selected promoters, or byseparating the promoters into different plant seeds. In thisimplementation, incorporating all selected promoters into one plant maybe advantageous, such that multiple plants with the same reporter can bein close proximity, therefore increasing the strength of a signalproduced by the reporters.

The percentage of sterile sentinel plant seeds in the seed mixture canbe manipulated to optimize the crop yield. Sterile sentinel plant seedswill result in a loss of yield for the farmer, as the sterile plantswill not produce fruit. However, the farm can use data collected fromthe sentinel plants to improve the yield of the next crop. For example,a farmer may plant a crop of corn with 100% non-sterile corn seed(“normal” corn seed) and may anticipate an average crop yield of 88%over a ten-year period given that 12% of the crop may be lost or fail onaverage over a long period of time due to diseases and other pressures.To increase yield over this period of time, the farmer may plant thefield with a mixture of 5% sterile sentinel corn seed and 95%non-sterile, non-sentinel corn seed. Though 5% yield from the field maybe initially lost due to application of sterile sentinel corn seed,these sterile sentinel plants may enable early detection and response tovarious pressures that previously resulted in 10% average yield loss ofthe crop over several years, and thus enable the farmer to reduce lossresulting from disease and other pressures to less than 1%, therebyincreasing total average yield over multiple years to approximately 94%.

In one implementation, the sterile sentinel plant seeds replace aportion of and/or all refuge seeds present in a seed mixture. Forexample, a seed mixture can be mixed to include a first percentage (e.g.2% to 10%) of sterile sentinel refuge seeds and a second percentage ofGMO seeds, the refuge seeds configured to prevent pathogen and weedresistance to the GMO seeds. In this example, sterile sentinel plantseeds can be incorporated in the seed mixture as the refuge seeds, thuslimiting any loss of crop yield due to implementation of sterilesentinel plants.

Similarly, stressors signaled by these sterile sentinel plants mayenable the farmer to enact rapid responses that initially reduce averagecrop loss from 10% to 5% such that the farmer initially achieves thesame average yield but enables the computer system to collect arelatively large amount of data from these deployed sterile sentinelplants. Over time, as the computer system collects additional stressorinformation from the field based on signals produced by deployed sterilesentinel plants over multiple seasons, the computer system can recommendsmaller ratios of sterile to non-sterile plants while continuing tooutput preemptive prompts to address early-stage stressors in the field,thereby enabling the farmer to reduce yield loss due to bothincorporation of sterile plants and stressors in the field and thusachieve higher average yield for the crop over time. Therefore, thecomputer system can indicate target minimum proportions of sterilesentinel plant seeds to non-sterile, non-sentinel seeds to plant in thefield in order to achieve minimum pressure sensing capabilities for longterm yield protection while minimizing immediate yield loss.

8.2 Non-Sterile Sentinel Plants

In one implementation, seeds for these sentinel plants are non-sterile.In this variation, non-sterile sentinel plant seeds can also be plantedin clusters—alongside non-sentinel plants bearing the same fruit or ofsimilar crop types—according to methods and techniques described abovefor sterile sentinel plant seeds in order to maintain highsignal-to-noise ratios and sensing capabilities for this crop whilelimiting total seeding cost (e.g., for sensing seeds of a greater costthan the non-sentinel seeds bearing the same fruit).

Alternatively, in this variation, sensing traits can be incorporatedinto a non-sterile GMO plant genome as part of a GMO stack alreadypresent in GMO seeds, which can then be planted to produce an entirecrop of sentinel plants. However, in this variation, these non-sterilesentinel plant seeds can be configured to generate several distinctsignals that represent an array of stresses and can be planted inclusters within the field—as described above—wherein all plants in onecluster contain the same promoter-reporter pair(s) configured to producea signal for a particular biotic or abiotic stressor (or a particularset of biotic and/or abiotic stressors). For example, non-sterilesentinel plant seeds containing the same promoter-reporter pairs areplanted along the full length of one crop row in the field withnon-sterile sentinel plant seeds in the two adjacent crops rowscontaining different promoter-reporter pairs configured to producesignals for different biotic or abiotic stressors; in this example, thispattern of rows containing seeds with different promoter-reporter pairsis repeated along the full length of the field. In another example,non-sterile sentinel plant seeds containing the same promoter-reporterpairs are planted in rectilinear clusters, such as in adjacentfive-meter-long segments of five consecutive crop rows with non-sterilesentinel plant seeds in the adjacent clusters containing differentpromoter-reporter pairs configured to produce signals for differentbiotic or abiotic stressors; in this example, this grid around ofclusters of non-sterile sentinel plants seeds containing the samepromoter-reporter pairs is repeated along the full length and width ofthe field.

By thus clustering non-sterile sentinel plants in one-dimensional ortwo-dimensional groups of plants configured to produce signals for thesame stressors, the crop as a whole can produce high-amplitudesignals—characterized by high signal-to-noise ratios—for multipledifferent biotic and/or abiotic stressors in discrete rows or regions ofthe field. As described above, stressors indicated by these rows orclusters of plants configured to produce signals for the same stressorscan then be interpolated or extrapolated across the entire field topredict pressures across the entire crop.

Therefore, in this variation, because each plant in the field exhibitssensing capabilities, the entire crop can be monitored directly, thecomputer system can generate a pressure map of biotic and/or abioticstressors for the crop as a whole based on signals produced by theseplants during one period of time (e.g., on one day) and detected by afixed or mobile local or remote sensors. By repeating this process todevelop new pressure maps for the field over time, the computer systemcan monitor stressors across the field over time and serve data and/orrecommendations for proactive mitigation of these stressors. Thecomputer system can also implement this process to update the pressuremap for the field following a stressor treatment at the field, therebyenabling a field operator to directly assess efficacy of this stressortreatment and to make more informed treatment decisions for the field inthe future. Further, once applying a particular treatment to the fieldbased on these interpreted pressures, the computer system can continueto measure and detect signals generated by the sentinel plants andtherefore assess efficacy of the particular treatment based on newpressures interpreted from these signals.

8.3 Plant Grafts

In one implementation, rather than planting the sentinel plants as seeds(such as in row crops), the sentinel plants can be grafted onto existingplants. Grafting may be useful for perennial crops and other high valuecrops, such as almond trees or grape vines. A scion or leafy portion ofthe sentinel plant may be grafted into a portion of the desired plant,for example on the middle portion of a tree trunk. For example, a scionof a sentinel grape vine can be grafted into the trunk of a maturegrape, such that the scion portion of the mature grape vine canimplement the sensing technology, providing a representation of thehealth of the mature grape vine As grafting sentinel plants intoexisting plants is, initially, a more time consuming process, thegrafting method may be useful for perennial crops, which do not requirereplanting each year. These plants are trimmed at the end of each seasonbut, when the leaves bloom the following season, the sensingcapabilities will still be present. Therefore, the grafts only need oneapplication to last the lifetime of the plant.

The location of sensors in these perennial or high value crops can alsobe optimized, similarly to the row crops. Multiple grafts can be appliedto one plant, to include each selected promoter and reporter in eachgrafted plant. Alternatively, specific reporter grafts can be selectedfor plants in different crop regions based on the likelihood of certainplant stressors appearing in different crop regions. As the grafts areapplied to grown plants, it may be beneficial to locate the sensors nearthe edges of a crop, for ease of application.

8.4 Controlled Environment Agriculture Applications

In one variation, sentinel plants can be grown in a controlledenvironment, such as a greenhouse (e.g., glass roof or factory farm) oranother enclosed growing structure. Sentinel plants grown in controlledenvironments can be regularly monitored for detection of pressures ofstressors at the sentinel plants. In one implementation, sentinel plantscan be grown in an enclosed growing structure via vertical farming.

Sentinel plants grown in these controlled environments can betransplanted to other locations (e.g., commercial agricultural fields)to serve as sentinel plants. Alternatively, sentinel plants grown incontrolled environments can be monitored for detection of pressures of astressor or stressors under particular controlled environmentalconditions (e.g., climate, region, presence of other plants) in thecontrolled environment. The computer system can interpret pressures inthese sentinel plants in the greenhouse environment and extract insightsinto plants (e.g., in an agricultural field) under similar environmentalconditions based on pressures in the sentinel plants.

The computer system can more frequently monitor sentinel plants in acontrolled environment than sentinel plants located in an agriculturalfield due to the smaller area of the greenhouse environment. Therefore,the computer system can extract further insights into these sentinelplants grown in the controlled environment. For example, by interpretingdaily pressures of a particular stressor in sentinel plants in agreenhouse, the computer system can more precisely converge on a modellinking features extracted from images collected of the sentinel plantsto pressures of the particular stressor. The computer system can thenbetter model pressures of the particular stressor in an agriculturalfield including sentinel plants of a same type and/or including thesesentinel plants once transplanted by a user associated with agriculturalfield.

9. Outputs

The computer system can: access images (e.g., spectral) of the sentinelplants; extract features indicative of stressors and pressurescorresponding to these stressors in these sentinel plants; interpolateor extrapolate pressures of particular stressors in these sentinelplants to other plants (e.g., sensor and non-sentinel plants) in thesame agricultural field (and in nearby fields); and then generatereal-time prompts or treatment decisions for these crops in order toincrease efficiency of crop treatments and maintenance over time andmaintain or increase yield from the agricultural field.

In one implementation, the computer system: extracts wavelengthmeasurements for specific compounds in a region of an image depicting acluster of sentinel plant; and transforms these wavelength measurementsinto a pressure (e.g., presence, magnitude) of a particular stressor orstressors in this cluster of sentinel plants. For example, if thecomputer system detects—in this region of the image—a specificwavelength for a compound associated with a fungal disease, the computersystem can access a model linking wavelength of the compound of interestto the fungal stressor and then pass the intensity of this wavelength inthis region of the image into the model to estimate the fungal pressure(e.g., in the form of “percent” pressure) in this cluster of sentinelplants. Based on the fungal pressure for the specific sentinel plant,the computer system can generate a prediction of the fungal pressure fornon-sentinel plants surrounding or nearby this cluster of sentinelplants.

In the foregoing example, to generate the model linking intensity ofwavelengths to pressures of stressors, a farmer may collect samples froma leaf or the soil sentinel plant to detect plant stressors. The samplescan be tested to identify the specific type and pressure of a stressorpresent at the leaf, while the wavelength of the compound in the plantsassociated with the disease can be measured from the images collected. Amodel depicting the relationship between the detected wavelength of thecompounds of interest and the pressure magnitude can then be generated(e.g., by the computer system) based on these empirical data.Subsequently, the computer system can automatically (and autonomously)predict pressures throughout the crop based on features extracted fromimages of the cluster of sentinel plants rather than based on physicalsamples collected by the farmer. Alternatively, this model can begenerated based on lab data prior to deployment of the sentinel plantsto the agricultural field and can be linked to deployed sentinel plantsduring the subsequent growing season.

In a crop with multiple clusters of sentinel plants or with sentinelplants distributed throughout the crop, the images collected both on theground and aerially can be accessed by the computer system to output apressure map for the crop. The pressure map can display the locations ofspecific disease and stressors, and can be updated or combined todisplay the spread or elimination of certain pressures over time. Themap can display interpolated pressure data for regions of the crop whereno sentinel plants are located. In one implementation, images can becollected multiple times per day from a camera located on a pole in thecenter of a cluster of sentinel plants. Additionally, satellite imagesof the entire crop, including other sentinel plant clusters, can becollected biweekly. The data collected daily from the single cluster canbe used to model the behavior of the other clusters, based on thebiweekly wavelength measurements of disease compounds in the rest of theclusters. The regions of the crop between clusters, or the“non-sentinel” regions, can also be modeled by interpolation (e.g., viamachine learning algorithms). To confirm presence of a stressor and tointerpret a pressure of this stressor, a farmer may collect samples ofthe sentinel plant itself or of the surrounding soil.

For example, the computer system can access a feed of images from aremote database, the first feed of images timestamped and georeferenced,and uploaded to the remote database via a wireless network from a devicelocated on a post in the center of a first cluster of sentinel plants inan agricultural field at a frequency of one image every hour; accesssatellite images of the agricultural field, including a set of clustersof sentinel plants, the satellite images collected biweekly; interpret apressure of a stressor in the first cluster based on the model linkingfeatures extracted from the feed of images to stressor and pressures ofstressors; interpolate the pressure of the set of clusters and of allplants (e.g., sterile and non-sterile plants) in the agricultural field,based on the model and the feed of images from the remote database andthe satellite images; generate a pressure map including locations of apressure in an agricultural field; magnitude of the pressure; locationsof sentinel plant clusters; a first timestamp indicating the time themap is generated and a second timestamp indicating a time for which themap is representative; generate prompts or treatment recommendations forthis agricultural field based on the pressure map; and, deliver thepressure map and corresponding prompts or treatment recommendations toan operator of the agricultural field.

After generating a pressure map based on the measured wavelengths ofspecific compounds in the plants, the computer system can prompt anoperator of the agricultural field to take certain actions in order tocombat plant stressors. In one implementation, a farmer may plant a rowof insect sentinel plant seeds on an edge of a soybean field, formonitoring the border between the farmer's crop and a neighboring crop.Each day, an optical device mounted to a pole in the row of sentinelplants can capture images of the sentinel plants. From these images, thecomputer system can measure the wavelengths of compounds associated withthe insect related disease, and display a certain insect pressuremagnitude on the edge of the map where the row of sentinel plants islocated. Based on the insect pressure magnitude and the times at whichimages were collected, the computer system can display a predictedcurrent insect pressure magnitude for the surrounding area in the cropand prompt the farmer to make certain decisions such as: whether totreat the crop with insecticide for the insects dependent on thepressure magnitude reading; which areas of the crop to treat for insectdisease; and an extent of treatment in different regions of the crop.After initial treatment, as more images are collected and more databecomes available, the computer system can update the pressure map andprompt the farmer to implement an updated treatment plan with this newinformation, and make improved treatment decisions for future insectrelated diseases. The output pressure map provides a means for thefarmer to be alerted to a disease or stress in the crop at the onset, aswell as access predictions for what may happen in response to certaintreatments or to applying no treatment. Over time, as more data iscollected and various treatments are applied to the crop based onstressors indicated by signals output by sentinel plants in the field,the computer system can develop models to predict responses of plantsand plant stressors to certain treatments, such as a magnitude change insignal output by a sentinel plant for a known stressor responsive to aparticular magnitude of treatment applied to the field.

The computer system can generate real-time prompts or treatmentdecisions for these crops in order to increase efficiency of croptreatments and maintenance over time and maintain or increase yield fromthe agricultural field. For example, in response to interpreting apressure of a particular stressor, in a set of sentinel plants, above athreshold pressure, the computer system can generate a prompt to addressthe particular stressor in plants proximal the set of sentinel plants.More specifically, the computer system can: isolate a first action, in aset of actions defined for sentinel plants, linked to the particularstressor; and transmit a notification to perform the first action in theagricultural field to mitigate the particular stressor to a computingdevice of a user associated with the agricultural field. Thus, thecomputer system can update users (e.g., agronomists, farmers, fieldowners) regarding plant health and/or suggest treatments for mitigatingpressures of stressors in plants.

9.1 Pressure Model

In one variation, as shown in FIG. 1 , the computer system can derive apressure model linking pressures of a particular stressor at a first setof sentinel plants (e.g., one sentinel plant, a cluster of sentinelplants) to pressures of the particular stressor at the second set ofsentinel plants. By developing this pressure model, the computer systemcan minimize data collection of all sentinel plants in a particularregion (e.g., agricultural field) by relating pressures in sentinelplants in a single set of sentinel plants to other sets of sentinelplants in the agricultural field.

For example, the computer system can: access a first feed of imagesrecorded at a first frequency by a fixed sensor (e.g., a camera mountedto a beam in a center of an agricultural field) facing a first set ofsentinel plants in an agricultural field; access a second image of asecond set of sentinel plants in the agricultural field, the secondimage recorded by a mobile sensor (e.g., camera of a mobile device of auser associated with the agricultural field) during a first time period;interpret a first pressure of a stressor in the first set of sentinelplants during the first time period based on a first set of featuresextracted from a first image, in the first feed of images, capturedduring the first time period; and interpret a second pressure of thestressor in the second set of sentinel plants during the first timeperiod based on a second set of features extracted from the secondimage. Based on the first pressure interpreted at the first set ofsentinel plants and the second pressure interpreted at the second set ofsentinel plants, the computer system can derive a pressure modelassociating pressure of the stressor at the first set of sentinel plantswith pressure of the stressor at the second set of sentinel plants.

Once the computer system derives the pressure model, the computer systemcan continue accessing images from the first feed to interpret pressuresat the first set of sentinel plants and at the second set of sentinelplants based on the model. For example, during a second time period, thecomputer system can: interpret a third pressure of the stressor in thefirst set of sentinel plants based on a third set of features extractedfrom a third image, in the first feed of images, captured during thesecond time period; and predict a fourth pressure of the stressor in thesecond set of sentinel plants during the second time period based on thethird pressure and the model. Therefore, the computer system can predictpressure at the second set of sentinel plants based on images of thefirst set of sentinel plants from the first feed, without accessingadditional images of the second set of sentinel plants. Alternatively,the computer system can continue collecting images of the second set ofsentinel plants at a second frequency less than the first frequency toensure precision of the pressure model and to update the pressure modelover time. Further, the computer system can collect images of other setsof sentinel plants and develop additional pressure models linkingpressures in sentinel plants of these other sets of sentinel plantsacross the particular region to the first set of sentinel plants in theagricultural field, thus enabling predictions of pressures of theparticular stressor in the set of sentinel plants across theagricultural field based on information extracted from images of thefirst set of sentinel plants.

Based on this predicted fourth pressure at the second set of sentinelplants, the computer system can generate a prompt or transmit anotification to a user associated with the agricultural field. Forexample, in response to the fourth pressure in the second set ofsentinel plants exceeding a threshold pressure, the computer system cangenerate a prompt to address the stressor in plants proximal the secondset of sentinel plants in the agricultural field.

9.2 Gradient Model

In one variation, as shown in FIGS. 2 and 3 , the computer system canderive a gradient model associating pressures of a particular stressorat a first set of sentinel plants (e.g., one sentinel plant, a clusterof sentinel plants) to pressures at subregions of an agricultural fieldincluding the first set of sentinel plants (e.g., a pressure gradient inthe agricultural field). By developing this gradient model, the computersystem can minimize data collection of all sentinel plants in aparticular region (e.g., agricultural field) by relating pressuregradients in the particular region (e.g., pressures in sentinel plantsacross the particular region) to a single set of sentinel plants in theagricultural field. Further, the computer system can correct fordeviations in pressures interpreted at the first set of sentinel plantsbased on the gradient model.

For example, the computer system can: access a first feed of imagesrecorded at a first frequency by a fixed sensor (e.g., a camera mountedto a pole in an agricultural field) facing a first set of sentinelplants in an agricultural field; access a second image of a region ofthe agricultural field comprising the first set of sentinel plants, thesecond image recorded by a mobile sensor (e.g., an aerial sensor, adrone, a satellite) during a first time period; interpret a firstpressure of a stressor in the first set of sentinel plants during thefirst time period based on a first set of features extracted from afirst image, in the first feed of images, captured during the first timeperiod; interpret a first pressure gradient of the stressor in sentinelplants in the region of the agricultural field during the first timeperiod based on a second set of features extracted from the secondimage; and derive a gradient model associating pressure of the stressorat the first set of sentinel plants and pressure gradient of thestressor in the region of the agricultural field based on the firstpressure of the stressor and the first pressure gradient.

Upon deriving the gradient model, the computer system can rectify thefirst pressure gradient based on the first pressure of the stressor atthe first set of sentinel plants and the gradient model. Further, thecomputer system can predict pressure gradients of the particularstressor based on features extracted from images in the first feed. Forexample, the computer system can: interpret a second pressure of thestressor in the first set of sentinel plants during a second time periodbased on a third set of features extracted from a third image, in thefirst feed of images, captured during the second time period; andpredict a second pressure gradient of the stressor in the region of theagricultural field during the second time period based on the secondpressure and the model.

From this pressure gradient, the computer system can monitor pressuresat various subregions of the agricultural field. If the computer systempredicts a high pressure of the particular stressor at a particularsubregion of the agricultural field, the computer system can flag thissubregion and generate a prompt to a user associated with theagricultural field to address the particular stressor in this subregion.For example, the computer system can, in response to the second pressuregradient predicting a third pressure in a subregion of the agriculturalfield and exceeding a threshold pressure, generate a prompt to addressthe stressor in plants occupying the agricultural field proximal thesubregion of the agricultural field. Further, based on the pressuregradient, the computer system can generate a pressure map. The computersystem can include this pressure map in the prompt for the user.

Further, the computer system can refine the gradient model byinterpreting pressures from additional sets of sentinel plants in theagricultural field. In one implementation, the entire agricultural fieldis sentinel plants (e.g., having no non-sentinel plants). In thisimplementation, the computer system interprets the first pressuregradient based on features extracted from the second image recorded by amobile sensor. The computer system can combine this low-resolutionpressure gradient data for the entire agricultural field of sentinelplants with the high-resolution pressure data for the first set ofsentinel plants to develop a more precise gradient model for predictingpressure gradients of the entire agricultural field.

In another implementation, in which clusters of sentinel plants areplanted within an agricultural field of non-sentinel plants, thecomputer system can interpret the first pressure gradient based onfeatures extracted from regions of the second image, recorded by themobile sensor, regions including the first set of sentinel plants and(at minimum) a second set of sentinel plants. In this implementation,the computer system can interpret a pressure of the particular stressorat the first set of sentinel plants based on the first image andinterpret a second pressure of the particular stressor at the first setof sentinel plants based on the second image. The computer system canthen: derive a gradient model associating pressure of the particularstressor at the first set of sentinel plants with pressure gradient ofthe first stressor in the agricultural field based on the secondpressure and the first pressure gradient, both extracted from the secondimage; and rectify the first pressure gradient of the particularstressor in the agricultural field based on the first pressure and themodel.

9.3 Annual Model

The computer system can leverage data corresponding to a particularagricultural field or crop to develop an annual model for modelingpressures of stressors in the particular agricultural field. Forexample, during a first season and for a particular crop, the computersystem can extract insights into: water movement across the particularcrop; sun exposure across the crop (e.g., daily, weekly, monthly,seasonally); and timing of pressures of other stressors such as insects,fungi, and nutrient deficiencies. The computer system can input each ofthese insights into an annual model for predicting conditions of thecrop at the beginning of next season and throughout the next season.Then, at the start of the next season, the computer system can predictinitial conditions of the crop based on the model. Further, the computersystem can suggest farming practices to a user associated with the cropbased on these predicted initial conditions, such as types of seedhybrid to plant and/or different blends of soil to lay. As the seasoncontinues, the system can update the annual model accordingly.

Further, based on the annual model, the computer system can predictand/or suggest agricultural products and/or treatments best suited forthis agricultural field. For example, the computer system can predict afirst pressure of a stressor in plants in the agricultural field at aparticular time based on the annual model. Based on the predicted firstpressure, the user may apply a new treatment to these plants at thebeginning of a season in order to mitigate the predicted first pressure.Later, the computer system can interpret a second pressure in plants inthe agricultural field at the particular time based on data recorded bya sensor in the agricultural field. If the second pressure is less thanthe predicted first pressure, the computer system can update the annualmodel accordingly and/or recommend the new treatment in the future totreat pressures of the stressor.

10. Single Sentinel Plant

In one variation, the computer system can extract insights from a singlesentinel plant (e.g., in a crop of non-sentinel plants, in a greenhouse)to: monitor pressures of stressors in plants in an agricultural field;develop models for predicting plant behavior over time; develop modelsfor predicting plant response to various stressors present at thesentinel plant; develop models for interpreting pressures of stressorsat the sentinel plant from measurements; testing efficacy of treatmentsfor various stressors present at the single sentinel plant; and/ordevelop models for plant response to these treatments.

For example, a single sentinel plant or a single cluster of sentinelplants can be grown in a crop of non-sentinel plants. This singlesentinel plant (or single cluster of sentinel plants) can be monitoredfor presence of stressors at the sentinel plant. For example, thecomputer system can access data (e.g., images) recorded by a sensor(e.g., a smartphone) and interpret a first pressure of a particularstressor at the sentinel plant based on features extracted from thisdata. Based on the interpreted first pressure at the single sentinelplant, the computer system can extract insights into plants proximal thesingle sentinel plant and/or within the crop of non-sentinel plants.Further, the computer system can suggest a particular treatment forplants in the crop based on the interpreted first pressure. Uponapplication of the particular treatment by a user, the computer systemcan interpret a second pressure to confirm efficacy of the particulartreatment.

In another example, a sentinel plant may be grown in a greenhouse. Thecomputer system can access data (e.g., hyperspectral images) recorded byan optical sensor in the greenhouse to extract a first set ofmeasurements (e.g., intensities of wavelengths) indicative of planthealth. A user (e.g., associated with the greenhouse) may collect asample from the sentinel plant to confirm health of the sentinel plantand/or presence of any stressors at the sentinel plant. In this example,if the user interprets the sentinel plant as healthy and interprets nopressures of a particular stressor present at the sentinel plant basedon the collected sample, the computer system can link the first set ofmeasurements to a healthy plant exhibiting no pressures of theparticular stressor and store this information into a model. Later, theuser may subject the sentinel plant to a pressure of the particularstressor (e.g., drought). The computer system can again access datarecorded by the optical sensor in the greenhouse to extract a second setof measurements (e.g., intensities of wavelengths) corresponding to thesentinel plant. The computer system can then link the second set ofmeasurements of the sentinel plant to the pressure of the particularstressor introduced by the user at the sentinel plant and store thisinformation into the model. Thus, over time, the computer system candevelop the model linking measurements extracted from data recorded bythe optical sensor in the greenhouse to pressures of the particularstressor at the sentinel plant.

In yet another example, the computer system can extract insights relatedto plant treatment efficacy over time. For example, a sentinel plant canbe grown in a greenhouse of plants arranged in vertical stacks (e.g.,via vertical farming). The computer system can extract measurements fromdata (e.g., images) recorded by a sensor in the greenhouse to extractinsights into plant health. The computer system can interpret a firstpressure of a particular stressor at the sentinel plant based on a firstset of measurements extracted from data recorded by the sensor at afirst time. The computer system can then notify a user associated withthe greenhouse of the first pressure. The user may then apply aparticular treatment to plants proximal the sentinel plant in thegreenhouse to mitigate the first pressure. Later, the computer systemcan interpret a second pressure of the particular stressor at thesentinel plant based on a second set of measurements extracted from datarecorded by the sensor at a second time (e.g., 24 hours afterapplication of the particular treatment). Based on the first and secondpressure, the computer system can derive a model representing pressuresof the particular stressor over time in response to application of theparticular treatment. The computer system can therefore derive modelsfor predicting plant responses to various treatments and/or agriculturaltechniques.

The computer systems and methods described herein can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a user computer or mobile device,wristband, smartphone, or any suitable combination thereof. Othercomputer systems and methods of the embodiment can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated by computer-executable components integrated with apparatusesand networks of the type described above. The computer-readable mediumcan be stored on any suitable computer readable media such as RAMs,ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives,floppy drives, or any suitable device. The computer-executable componentcan be a processor but any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

I claim:
 1. A method comprising: accessing a feed of images of regionsof an agricultural environment comprising: a first set of sentinelplants of a sentinel plant type located in a first region of theagricultural environment, sentinel plants of the sentinel plant typecomprising a first promoter-reporter pair, in a set of promoter-reporterpairs, configured to signal pressures of a first stressor, in a set ofstressors, at sentinel plants of the sentinel plant type; and a secondset of sentinel plants of the sentinel plant type located in a secondregion of the agricultural environment; interpreting a first pressure ofthe first stressor in the first region of the agricultural environmentduring a first time period based on a first set of features extractedfrom a first subset of images, in the feed of images, of the first setof sentinel plants and captured during the first time period;interpreting a second pressure of the first stressor in the secondregion during the first time period based on a second set of featuresextracted from a second subset of images, in the feed of images, of thesecond set of sentinel plants and captured during the first time period;and generating a first pressure map depicting pressures of the set ofstressors in the agricultural environment during the first time periodbased on the first pressure in the first region and the second pressurein the second region.
 2. A method comprising: accessing a feed of imagesof a first set of sentinel plants in an agricultural environment, thefirst set of sentinel plants comprising a set of promoter-reporterpairs: configured to signal pressures of a set of stressors at sentinelplants in the first set of sentinel plants; and comprising a firstpromoter-reporter pair configured to signal pressures of a firststressor in the set of stressors; interpreting a first pressure of thefirst stressor at the first set of sentinel plants during a first timeperiod based on a first set of features extracted from a first image, inthe feed of images, captured during the first time period; accessing asecond image, recorded during the first time period, of a region of theagricultural environment comprising the first set of sentinel plants;interpreting a first pressure gradient of the first stressor across theregion of the agricultural environment during the first time periodbased on a second set of features extracted from the second image;deriving a model associating pressure of the first stressor at the firstset of sentinel plants and pressure gradient of the first stressor inthe region of the agricultural environment based on the first pressureand the first pressure gradient; interpreting a second pressure of thefirst stressor at the first set of sentinel plants during a second timeperiod succeeding the first time period based on a third set of featuresextracted from a third image captured during the second time period; andpredicting a second pressure gradient of the first stressor in theregion of the agricultural environment during the second time periodbased on the second pressure and the model.